Investors discuss the game-changing opportunity of generative AI beyond ChatGPT

Previous machine learning was like rubbing two sticks together. This is like a flamethrower.

Tim Tully, Menlo Ventures  
 

A wide-open landscape for evolution

In terms of driving investment and growth, Duesterhoeft pointed out while most use cases for generative AI are still in early, experimental stages, as these tools mature, their growth potential could be comparable to — or even eclipse — the level of activity driven by cloud technology. Das imagines that the evolution of generative AI could follow a similar trajectory to the adoption of web browsers a few decades ago. Netscape may have been the first web browser consumers used to access the internet outside of portals like Prodigy or AOL, but ultimately, companies like Microsoft and Google effectively monetized the browser. ChatGPT may have introduced the general public to generative AI, but other use cases and business models have yet to emerge.

Conforti noted that at the enterprise level, CIOs know they need to have an AI strategy, but most have yet to make decisive plans to create value from the technology. First, companies need to determine how to leverage their data to train generative AI models. How quickly a business moves forward in this space will depend on the nature of their data, the degree of regulatory scrutiny in their industry, and how willing they are to train their own proprietary models. In the meantime, companies are increasing their spending on analytics tools as well as data cleaning, prep, and prioritization as they anticipate developing and executing AI strategies using their proprietary data.

Identifying investment opportunities in generative AI

When it comes to picking winners in the competitive (and rapidly growing) field of generative AI companies, public and private investors look at a variety of factors. One is incumbency — a large, established user base and distribution network is a substantial advantage. “Adobe is going to put everything in their suite, and it’s going to be hard to compete with it,” said Conforti.

For Tully, generative AI tools that function at the level of foundational infrastructure are among the most attractive investments, because once they are embedded in an enterprise’s tech stack, they’re unlikely to be abandoned or replaced. Application-layer tools that end users rely on to do their jobs or “live and die by every day” are also compelling.

The panelists also identified vertical software applications of generative AI technology as an area of opportunity. “There’s a lot to be said for really deeply understanding a particular vertical, fine-tuning the model with vertical-specific data, and then being able to own that stack much more deeply than you can on the horizontal side,” said Duesterhoeft. In addition to ChatGPT, panelists mentioned vector database Pinecone and branded content generator Typeface as examples of disruptors in the generative AI space.

Of course, as with any emerging technology, generative AI products come with their own set of obstacles and challenges, including big questions around privacy, security, and governance. But Tech Leadership Forum panelists agreed unanimously that chatbots like ChatGPT are only the beginning. As Conforti summed it up: “Is the end state for generative AI a chatbot? No, it’s just the example that we all understood very plainly, that got everybody thinking, “Wow, this is going to change my life. I have no idea what it’s going to look like, but I think it’ll be profound.”

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